OpenRaiser/Intern-Atlas huggingface dataset:
Distributed Application of Guideline-Based Decision Support through Mobile Devices: Implementation and Evaluation
Venue: arXiv cs.AI 2021
Year: 2021

Traditionally Guideline(GL)based Decision Support Systems (DSSs) use a centralized infrastructure to generate recommendations to care providers. However, managing patients at home is preferable, reducing costs and empowering patients. We aimed to design, implement, and demonstrate the feasibility of a new architecture for a distributed DSS that provides patients with personalized, context-sensitive, evidence based guidance through their mobile device, and increases the robustness of the distributed application of the GL, while maintaining access to the patient longitudinal record and to an up to date evidence based GL repository. We have designed and implemented a novel projection and callback (PCB) model, in which small portions of the evidence based GL procedural knowledge, adapted to the patient preferences and to their current context, are projected from a central DSS server, to a local DSS on the patient mobile device that applies that knowledge. When appropriate, as defined by a temporal pattern within the projected plan, the local DSS calls back the central DSS, requesting further assistance, possibly another projection. Thus, the GL specification includes two levels: one for the central DSS, one for the local DSS. We successfully evaluated the PCB model within the MobiGuide EU project by managing Gestational Diabetes Mellitus patients in Spain, and Atrial Fibrillation patients in Italy. Significant differences exist between the two GL representations, suggesting additional ways to characterize GLs. Mean time between the central and local interactions was quite different for the two GLs: 3.95 days for gestational diabetes, 23.80 days for atrial fibrillation. Most interactions, 83%, were due to projections to the mDSS. Others were data notifications, mostly to change context. Robustness was demonstrated through successful recovery from multiple local DSS crashes.
Covert MIMO Communications under Variational Distance Constraint
Venue: arXiv cs.IT 2021
Year: 2021

The problem of covert communication over Multiple-Input Multiple-Output (MIMO) Additive White Gaussian Noise (AWGN) channels is investigated, in which a transmitter attempts to reliably communicate with a legitimate receiver while avoiding detection by a passive adversary. The covert capacity of the MIMO AWGN is characterized under a variational distance covertness constraint when the MIMO channel matrices are static and known. The characterization of the covert capacity is also extended to a class of channels in which the legitimate channel matrix is known but the adversary's channel matrix is only known up to a rank and a spectral norm constraint.
HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem Solving
Venue: arXiv cs.LG 2021
Year: 2021

Humans learn compositional and causal abstraction, \ie, knowledge, in response to the structure of naturalistic tasks. When presented with a problem-solving task involving some objects, toddlers would first interact with these objects to reckon what they are and what can be done with them. Leveraging these concepts, they could understand the internal structure of this task, without seeing all of the problem instances. Remarkably, they further build cognitively executable strategies to \emph{rapidly} solve novel problems. To empower a learning agent with similar capability, we argue there shall be three levels of generalization in how an agent represents its knowledge: perceptual, conceptual, and algorithmic. In this paper, we devise the very first systematic benchmark that offers joint evaluation covering all three levels. This benchmark is centered around a novel task domain, HALMA, for visual concept development and rapid problem-solving. Uniquely, HALMA has a minimum yet complete concept space, upon which we introduce a novel paradigm to rigorously diagnose and dissect learning agents' capability in understanding and generalizing complex and structural concepts. We conduct extensive experiments on reinforcement learning agents with various inductive biases and carefully report their proficiency and weakness.
Practical Mutation Testing at Scale
Venue: arXiv cs.SE 2021
Year: 2021

Mutation analysis assesses a test suite's adequacy by measuring its ability to detect small artificial faults, systematically seeded into the tested program. Mutation analysis is considered one of the strongest test-adequacy criteria. Mutation testing builds on top of mutation analysis and is a testing technique that uses mutants as test goals to create or improve a test suite. Mutation testing has long been considered intractable because the sheer number of mutants that can be created represents an insurmountable problem -- both in terms of human and computational effort. This has hindered the adoption of mutation testing as an industry standard. For example, Google has a codebase of two billion lines of code and more than 500,000,000 tests are executed on a daily basis. The traditional approach to mutation testing does not scale to such an environment. To address these challenges, this paper presents a scalable approach to mutation testing based on the following main ideas: (1) Mutation testing is done incrementally, mutating only changed code during code review, rather than the entire code base; (2) Mutants are filtered, removing mutants that are likely to be irrelevant to developers, and limiting the number of mutants per line and per code review process; (3) Mutants are selected based on the historical performance of mutation operators, further eliminating irrelevant mutants and improving mutant quality. Evaluation in a code-review-based setting with more than 24,000 developers on more than 1,000 projects shows that the proposed approach produces orders of magnitude fewer mutants and that context-based mutant filtering and selection improve mutant quality and actionability. Overall, the proposed approach represents a mutation testing framework that seamlessly integrates into the software development workflow and is applicable up to large-scale industrial settings.
Procam Calibration from a Single Pose of a Planar Target
Venue: arXiv cs.CV 2021
Year: 2021

A novel user friendly method is proposed for calibrating a procam system from a single pose of a planar chessboard target. The user simply needs to orient the chessboard in a single appropriate pose. A sequence of Gray Code patterns are projected onto the chessboard, which allows correspondences between the camera, projector and the chessboard to be automatically extracted. These correspondences are fed as input to a nonlinear optimization method that models the projector of the principle points onto the chessboard, and accurately calculates the intrinsic and extrinsic parameters of both the camera and the projector, as well as the camera's distortion coefficients. The method is experimentally validated on the procam system, which is shown to be comparable in accuracy with existing multi-pose approaches. The impact of the orientation of the chessboard with respect to the procam imaging places is also explored through extensive simulation.
Quasi-Distributed Antenna Selection for Spectral Efficiency Maximization in Subarray Switching XL-MIMO Systems
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we consider the downlink (DL) of a zero-forcing (ZF) precoded extra-large scale massive MIMO (XL-MIMO) system. The base-station (BS) operates with limited number of radio-frequency (RF) transceivers due to high cost, power consumption and interconnection bandwidth associated to the fully digital implementation. The BS, which is implemented with a subarray switching architecture, selects groups of active antennas inside each subarray to transmit the DL signal. This work proposes efficient resource allocation (RA) procedures to perform joint antenna selection (AS) and power allocation (PA) to maximize the DL spectral efficiency (SE) of an XL-MIMO system operating under different loading settings. Two metaheuristic RA procedures based on the genetic algorithm (GA) are assessed and compared in terms of performance, coordination data size and computational complexity. One algorithm is based on a quasi-distributed methodology while the other is based on the conventional centralized processing. Numerical results demonstrate that the quasi-distributed GA-based procedure results in a suitable trade-off between performance, complexity and exchanged coordination data. At the same time, it outperforms the centralized procedures with appropriate system operation settings.
Neuroscience-Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems
Venue: arXiv cs.NE 2021
Year: 2021

If machine failures can be detected preemptively, then maintenance and repairs can be performed more efficiently, reducing production costs. Many machine learning techniques for performing early failure detection using vibration data have been proposed; however, these methods are often power and data-hungry, susceptible to noise, and require large amounts of data preprocessing. Also, training is usually only performed once before inference, so they do not learn and adapt as the machine ages. Thus, we propose a method of performing online, real-time anomaly detection for predictive maintenance using Hierarchical Temporal Memory (HTM). Inspired by the human neocortex, HTMs learn and adapt continuously and are robust to noise. Using the Numenta Anomaly Benchmark, we empirically demonstrate that our approach outperforms state-of-the-art algorithms at preemptively detecting real-world cases of bearing failures and simulated 3D printer failures. Our approach achieves an average score of 64.71, surpassing state-of-the-art deep-learning (49.38) and statistical (61.06) methods.
Optimal Sorting Circuits for Short Keys
Venue: arXiv cs.DS 2021
Year: 2021

A long-standing open question in the algorithms and complexity literature is whether there exist sorting circuits of size $o(n \log n)$. A recent work by Asharov, Lin, and Shi (SODA'21) showed that if the elements to be sorted have short keys whose length $k = o(\log n)$, then one can indeed overcome the $n\log n$ barrier for sorting circuits, by leveraging non-comparison-based techniques. More specifically, Asharov et al.~showed that there exist $O(n) \cdot \min(k, \log n)$-sized sorting circuits for $k$-bit keys, ignoring $poly\log^*$ factors. Interestingly, the recent works by Farhadi et al. (STOC'19) and Asharov et al. (SODA'21) also showed that the above result is essentially optimal for every key length $k$, assuming that the famous Li-Li network coding conjecture holds. Note also that proving any {\it unconditional} super-linear circuit lower bound for a wide class of problems is beyond the reach of current techniques. Unfortunately, the approach taken by Asharov et al.~to achieve optimality in size somewhat crucially relies on sacrificing the depth: specifically, their circuit is super-{\it poly}logarithmic in depth even for 1-bit keys. Asharov et al.~phrase it as an open question how to achieve optimality both in size and depth. In this paper, we close this important gap in our understanding. We construct a sorting circuit of size $O(n) \cdot \min(k, \log n)$ (ignoring $poly\log^*$ terms) and depth $O(\log n)$. To achieve this, our approach departs significantly from the prior works. Our result can be viewed as a generalization of the landmark result by Ajtai, Komlós, and Szemerédi (STOC'83), simultaneously in terms of size and depth. Specifically, for $k = o(\log n)$, we achieve asymptotical improvements in size over the AKS sorting circuit, while preserving optimality in depth.
Data Quality Certification using ISO/IEC 25012: Industrial Experiences
Venue: arXiv cs.DB 2021
Year: 2021

The most successful organizations in the world are data-driven businesses. Data is at the core of the business of many organizations as one of the most important assets, since the decisions they make cannot be better than the data on which they are based. Due to this reason, organizations need to be able to trust their data. One important activity that helps to achieve data reliability is the evaluation and certification of the quality level of organizational data repositories. This paper describes the results of the application of a data quality evaluation and certification process to the repositories of three European organizations belonging to different sectors. We present findings from the point of view of both the data quality evaluation team and the organizations that underwent the evaluation process. In this respect, several benefits have been explicitly recognised by the involved organizations after achieving the data quality certification for their repositories (e.g., long-term organizational sustainability, better internal knowledge of data, and a more efficient management of data quality). As a result of this experience, we have also identified a set of best practices aimed to enhance the data quality evaluation process.
Multi-Knowledge Fusion for New Feature Generation in Generalized Zero-Shot Learning
Venue: arXiv cs.CV 2021
Year: 2021

Suffering from the semantic insufficiency and domain-shift problems, most of existing state-of-the-art methods fail to achieve satisfactory results for Zero-Shot Learning (ZSL). In order to alleviate these problems, we propose a novel generative ZSL method to learn more generalized features from multi-knowledge with continuously generated new semantics in semantic-to-visual embedding. In our approach, the proposed Multi-Knowledge Fusion Network (MKFNet) takes different semantic features from multi-knowledge as input, which enables more relevant semantic features to be trained for semantic-to-visual embedding, and finally generates more generalized visual features by adaptively fusing visual features from different knowledge domain. The proposed New Feature Generator (NFG) with adaptive genetic strategy is used to enrich semantic information on the one hand, and on the other hand it greatly improves the intersection of visual feature generated by MKFNet and unseen visual faetures. Empirically, we show that our approach can achieve significantly better performance compared to existing state-of-the-art methods on a large number of benchmarks for several ZSL tasks, including traditional ZSL, generalized ZSL and zero-shot retrieval.
Relative induction principles for type theories
Venue: arXiv cs.LO 2021
Year: 2021

We present new induction principles for the syntax of dependent type theories, which we call relative induction principles. The result of the induction principle relative to a functor F into the syntax is stable over the codomain of F. We rely on the internal language of presheaf categories. In order to combine the internal languages of multiple presheaf categories, we use Dependent Right Adjoints and Multimodal Type Theory. Categorical gluing is used to prove these induction principles, but it not visible in their statements, which involve a notion of model without context extensions. As example applications of these induction principles, we give short and boilerplate-free proofs of canonicity and normalization for some small type theories, and sketch proofs of other metatheoretic results.
Automating Test Case Identification in Java Open Source Projects on GitHub
Venue: arXiv cs.SE 2021
Year: 2021

Software testing is one of the very important Quality Assurance (QA) components. A lot of researchers deal with the testing process in terms of tester motivation and how tests should or should not be written. However, it is not known from the recommendations how the tests are written in real projects. In this paper, the following was investigated: (i) the denotation of the word "test" in different natural languages; (ii) whether the number of occurrences of the word "test" correlates with the number of test cases; and (iii) what testing frameworks are mostly used. The analysis was performed on 38 GitHub open source repositories thoroughly selected from the set of 4.3M GitHub projects. We analyzed 20,340 test cases in 803 classes manually and 170k classes using an automated approach. The results show that: (i) there exists a weak correlation (r = 0.655) between the number of occurrences of the word "test" and the number of test cases in a class; (ii) the proposed algorithm using static file analysis correctly detected 97% of test cases; (iii) 15% of the analyzed classes used main() function whose represent regular Java programs that test the production code without using any third-party framework. The identification of such tests is very complex due to implementation diversity. The results may be leveraged to more quickly identify and locate test cases in a repository, to understand practices in customized testing solutions, and to mine tests to improve program comprehension in the future.
Rethinking Natural Adversarial Examples for Classification Models
Venue: arXiv cs.CV 2021
Year: 2021

Recently, it was found that many real-world examples without intentional modifications can fool machine learning models, and such examples are called "natural adversarial examples". ImageNet-A is a famous dataset of natural adversarial examples. By analyzing this dataset, we hypothesized that large, cluttered and/or unusual background is an important reason why the images in this dataset are difficult to be classified. We validated the hypothesis by reducing the background influence in ImageNet-A examples with object detection techniques. Experiments showed that the object detection models with various classification models as backbones obtained much higher accuracy than their corresponding classification models. A detection model based on the classification model EfficientNet-B7 achieved a top-1 accuracy of 53.95%, surpassing previous state-of-the-art classification models trained on ImageNet, suggesting that accurate localization information can significantly boost the performance of classification models on ImageNet-A. We then manually cropped the objects in images from ImageNet-A and created a new dataset, named ImageNet-A-Plus. A human test on the new dataset showed that the deep learning-based classifiers still performed quite poorly compared with humans. Therefore, the new dataset can be used to study the robustness of classification models to the internal variance of objects without considering the background disturbance.
Coronary Artery Disease Research in India: A Scientometric Assessment of Publication during 1990-2019
Venue: arXiv cs.DL 2021
Year: 2021

The present study examined 4698 Indian Coronary Artery Disease research publications, as indexed in Web of Science database during 1990-2019, with a view to understand their growth rate, global share, citation impact, international collaborative papers, distribution of publications by broad subjects, productivity and citation profile of top organizations and authors, and preferred media of communication. The Indian publications registered an annual average growth rate of 11.47%, global share of 1.14%, international collaborative publications share of 38.89% and its citation impact averaged to 25.58 citations per paper. Among broad subjects, Cardiovascular System & Cardiology contributed the largest publications share of 19.14% in Indian coronary artery disease output, followed by Neurosciences & Neurology (14.94%), Pharmacology & Pharmacy (8.51%), etc. during 1990-2019. Among various organizations and authors contributing to Indian coronary artery disease research, the top 20 organizations and top 30 authors together contributed 40.70% and 37.29% respectively as their share of Indian publication output and 38.36% and 33.13% respectively as their share of Indian citation output during 1990-2019. Among 1222 contributing journals in Indian coronary artery disease research, the top 30 journals registered 30.80% share during 1990-2019. There is an urgent need to increase the publication output, improve research quality and improve international collaboration. Indian government also needs to come up with a policy for identification, screening, diagnosis and treatment of coronary artery disease patients, besides curriculum reform in teaching, capacity building, patient education and political support are badly needed.
Quantum Entropic Causal Inference
Venue: arXiv cs.ET 2021
Year: 2021

The class of problems in causal inference which seeks to isolate causal correlations solely from observational data even without interventions has come to the forefront of machine learning, neuroscience and social sciences. As new large scale quantum systems go online, it opens interesting questions of whether a quantum framework exists on isolating causal correlations without any interventions on a quantum system. We put forth a theoretical framework for merging quantum information science and causal inference by exploiting entropic principles. At the root of our approach is the proposition that the true causal direction minimizes the entropy of exogenous variables in a non-local hidden variable theory. The proposed framework uses a quantum causal structural equation model to build the connection between two fields: entropic causal inference and the quantum marginal problem. First, inspired by the definition of geometric quantum discord, we fill the gap between classical and quantum conditional density matrices to define quantum causal models. Subsequently, using a greedy approach, we develop a scalable algorithm for quantum entropic causal inference unifying classical and quantum causality in a principled way. We apply our proposed algorithm to an experimentally relevant scenario of identifying the subsystem impacted by noise starting from an entangled state. This successful inference on a synthetic quantum dataset can have practical applications in identifying originators of malicious activity on future multi-node quantum networks as well as quantum error correction. As quantum datasets and systems grow in complexity, our framework can play a foundational role in bringing observational causal inference from the classical to the quantum domain.
Using Fault Injection on the Nanosatellite Subsystems Integration Testing
Venue: arXiv cs.OH 2021
Year: 2021

Since the 2000's, an increased number of nanosatellites have accessed space. However, studies show that the number of unsuccessful nanosatellite missions is very expressive. Moreover, these statistics are correlated to poor verification and validation processes used by hobbyists satellite developers because major space agencies keep high successful ratings even with small/nano satellites missions due to its rigorous V\&V processes. Aiming to improve payloads integration testing of NanosatC-BR-2, a 2-U Cubesat based nanosatellite under development by INPE, the fault injection technique has been used. It is very useful technique to test systems prototypes. We present the design and implementation of a Failure Emulator Mechanism (FEM) on I2C communication bus for testing the interaction among the NCBR2 subsystems, supporting interoperability and robustness requirements verification. The FEM is modelled to work at the communication bus emulating eventual faults of the communicating subsystems in the messages exchanged. Using an Arduino board for the FEM and NI LabView environment it is possible to program the mechanism to inject different faults at the I2C bus during different operation modes. Based on a serial architecture, the FEM will be able to intercept all messages and implement different faults as service and timing faults. The FEM interface with the tester is designed in LabView environment. Control and observation facilities are available to generate and upload the faultload script to FEM Arduino board. The proposed FEM architecture and its implementation are validated using two subsystems under testing prototypes: the OnBoard Data Handling Computer and the Langmuir Probe NCBR2 payload. For this analysis purpose, the prototypes simulate in two different Arduinos boards the expected behavior of each subsystem in the communication.